Research on intelligent tunnel support method based on IPSO-MOC-RF algorithm
The idea of intelligent tunnel support is an effective solution to the problem of low matching degree between tunnel support parameters and actual geological conditions.However,the research and implementation of tunnel intelligent support method are few,and the establishment and application of this method is in great demand.In this paper,by improving random forest algorithm and particle swarm optimization algorithm,an IPSO-MOC-RF intelligent decision algorithm adapted to multi-label and multi-output data types was proposed to analyze the complex and diverse support parameters of tunnels.The tunnel support parameter system was established according to three research scenarios of tunnel survey and design stage,construction stage and rock burst disaster,and 285,480 and 543 data samples were collected respectively for training models.According to the training results,the MR values were 0.886,0.917,0.897,and Hamming Loss values were 0.046-0.091,respectively,which verified that the model could effectively extract the complex nonlinear mapping characteristics between geological indicators and tunnel support parameters.In addition,a tunnel intelligent support platform was established by integrating BIM model and intelligent decision model with Cesium cloud platform,and applied to the Grand Canyon tunnel of the Ehan expressway in China.The validity of the intelligent tunnel support decision is verified by statistical analysis,theoretical analysis and field continuous monitoring.The results show that only 1 of the 7 samples in the survey and design stage failed;only 3 of the 47 samples in the construction stage failed,and the 3 selected test sections were all successful(the maximum displacement of the tunnel vault and side wall were 36.4 mm and 25.8 mm,respectively);the three test sections of rockburst disasters were successful.The application effect of intelligent tunnel support model is remarkable,which verifies the feasibility of the practical application of this method,and provides a new idea for the research of intelligent tunnel construction.
tunnel engineeringintelligent support methodsupport parametermulti-label multi-outputrandom forest
NETL
NSTL
万方数据
